Review of Automatic Semantic Rule Based Coordination for Content Extraction in Videos Using Fuzzy Ontology

data mining

The ultimate goal is to enable users to retrieve some desired Contents from massive amounts of video data in an efficient and semantically meaningful manner. There are three levels of video content which are raw video data, low-level features and semantic content. First raw video data consist of elementary physical video units together with some general video attributes such as Format, length, and frame rate. Second, low- level features are characterized by audio, text, and visual features such as Texture, color distribution, shape, motion, etc.Recent advances in digital video analysis and retrieval have made video more accessible than ever. The representation and recognition of events in a video is important for a number of tasks such as video surveillance, video browsing and content based video indexing. Raw data and low-level features alone are not sufficient to fulfill the user’s needs; that is, a deeper understanding of the content at the semantic level is required .Currently, manual techniques, which are inefficient, subjective and costly in time and limit the querying capabilities .Here, we propose a semantic content extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted automatically.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.